RDA-SHARC’s Open Science Rewards and Incentives Survey

This survey addresses awareness of Open Science activities and existing & expected reward systems in research assessment, in particular for sharing activities, in different contexts.

Quick summary of respondents background: We received 230 responses, from people working in 37 countries, of which 41.3% declare their gender was female, 35.65% male, 1.3% non-binary or gender-queer and 21.74% had no response. Most respondents had ‘Researcher’ as their main job title (40.87%), were affiliated to a ‘University’ (39.13%) or a ‘Research institute’ (37.39%), and had between 10 and 20 years of experience in their field (32.17%).

Section 1

Are you familiar with Open Science?

Code

survey %>%
    group_by(are_you_familiar_with_open_science) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(n/sum(n))) %>%
    rename(`Are you familiar with Open Science?` = are_you_familiar_with_open_science) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Are you familiar with Open Science?	n	%
No	22	10%
Yes	208	90%

Code

# general
survey %>%
    group_by(are_you_familiar_with_open_science) %>%
    count() %>%
    ggplot(aes(y = n, x = "", fill = are_you_familiar_with_open_science)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Set1") +
    theme_light() + labs(fill = "Are you familiar\nwith Open Science?",
    x = "", y = "")

Are you involved in some of the following Open Science activities?

Code

# Select only the likert items in the
# survey
items_involved <- select(survey, starts_with("are_you_involved_in_some_of"))

# Rename the items so that the question
# statement becomes the name
names(items_involved) <- c("Sharing a research manuscript as a preprint",
    "Publishing a paper or monograph book as open access",
    "Preregistration of the study design, methods, hypothesis etc., prior to commencing the research",
    "Open or FAIR data management and sharing (for research data, software, models, algorithms, workflows etc.)",
    "Participation in open peer review (being reviewed or the reviewer)",
    "Participation in public engagement, including citizen or community science",
    "Collaboration via virtual research environments or virtual laboratories",
    "None of the above")

# We don't want this option
items_involved <- items_involved %>%
    select(-`None of the above`)

# A custom function to recode numerical
# responses into ordered factors
likert_recode_yn <- function(x) {
    y <- factor(x, levels = c("No", "Yes"),
        exclude = "N/A")
    return(y)
}

# Transform the items into factors and
# save the data set as a likert object
items_involved <- items_involved %>%
    mutate_all(likert_recode_yn) %>%
    as.data.frame() %>%
    likert::likert()

# percentages
items_involved$results %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position")) %>%
    kableExtra::column_spec(1, width = "30em")

Item	No	Yes
Sharing a research manuscript as a preprint	47	53
Publishing a paper or monograph book as open access	37	63
Preregistration of the study design, methods, hypothesis etc., prior to commencing the research	84	16
Open or FAIR data management and sharing (for research data, software, models, algorithms, workflows etc.)	47	53
Participation in open peer review (being reviewed or the reviewer)	60	40
Participation in public engagement, including citizen or community science	50	50
Collaboration via virtual research environments or virtual laboratories	72	28

Code

# plot
likert.bar.plot(items_involved, plot.percent.low = FALSE,
    plot.percent.high = FALSE, col = c("#D7B365",
        "#59B4AB"), wrap = 40, plot.percents = TRUE) +
    ggpubr::theme_cleveland() + theme(legend.position = "bottom")

Are you familiar with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles?

Code

survey %>%
    group_by(are_you_familiar_with_the_fair_findable_accessible_interoperable_and_reusable_principles_defined_here) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(n/sum(n))) %>%
    rename(Responses = are_you_familiar_with_the_fair_findable_accessible_interoperable_and_reusable_principles_defined_here) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Responses	n	%
No	60	26%
Yes	170	74%

Code

# general
survey %>%
    group_by(are_you_familiar_with_the_fair_findable_accessible_interoperable_and_reusable_principles_defined_here) %>%
    count() %>%
    ggplot(aes(y = n, x = "", fill = are_you_familiar_with_the_fair_findable_accessible_interoperable_and_reusable_principles_defined_here)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Set1") +
    theme_light() + labs(fill = "Are you familiar\nwith the FAIR principles?",
    x = "", y = "")

Are you involved in some steps of data FAIRification process?

Code

survey %>%
    group_by(are_you_involved_in_some_steps_of_data_fai_rification_process_such_as_those_referred_to_here) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(n/sum(n))) %>%
    rename(Responses = are_you_involved_in_some_steps_of_data_fai_rification_process_such_as_those_referred_to_here) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Responses	n	%
N/A	62	27.0%
No	94	40.9%
Yes	74	32.2%

Code

# general
survey %>%
    rename(response = are_you_involved_in_some_steps_of_data_fai_rification_process_such_as_those_referred_to_here) %>%
    group_by(response) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = fct_relevel(response,
        "No", "Yes", "N/A"))) + geom_bar(stat = "identity",
    position = "fill") + scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Set1") +
    theme_light() + labs(fill = "Are you involved in\nsome steps of data\nFAIRification process?",
    x = "", y = "")

Does your institute/organisation have policies on various Open Science activities?

Code

survey %>%
    group_by(does_your_institute_organisation_have_policies_on_various_open_science_activities) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(n/sum(n))) %>%
    rename(Responses = does_your_institute_organisation_have_policies_on_various_open_science_activities) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Responses	n	%
N/A	6	3%
No	98	43%
Yes	126	55%

Code

# general
survey %>%
    rename(response = does_your_institute_organisation_have_policies_on_various_open_science_activities) %>%
    group_by(response) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = fct_relevel(response,
        "No", "Yes", "N/A"))) + geom_bar(stat = "identity",
    position = "fill") + scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Set1") +
    theme_light() + labs(fill = "Does your institute/\norganisation have policies\non various Open Science\nactivities?",
    x = "", y = "")

Section 2

Could you please specify to what extent you feel the following activities should be credited / rewarded?

Rewards may include career promotion, grants/funding/prizes, gained credits in a research evaluation procedure, authorship/ contributorship, increased academic visibility.

Code

# Select only the likert items in the
# survey
items <- select(survey, starts_with(c("could_you_please_specify_to_what_extent")))

# Rename the items so that the question
# statement becomes the name
names(items) <- c("Sharing a research manuscript as a preprint",
    "Publishing a paper or monograph book as open access",
    "Preregistration of the study design, methods, hypothesis etc., prior to commencing the research",
    "Open or FAIR data management and sharing (for research data, software, models, algorithms, workflows etc.)",
    "Participation in open peer review (being reviewed or the reviewer)",
    "Participation in public engagement, including citizen or community science",
    "Collaboration via virtual research environments or virtual laboratories")

# A custom function to recode numerical
# responses into ordered factors
likert_recode <- function(x) {
    y <- factor(x, levels = c("Definitely Not",
        "Probably Not", "Possibly", "Very Probably",
        "Definitely"), exclude = "No opinion")
    return(y)
}

# Transform the items into factors and
# save the data set as a likert object
items_likert <- items %>%
    mutate_all(likert_recode) %>%
    as.data.frame() %>%
    likert::likert()

# percentages
items_likert$results %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position")) %>%
    kableExtra::column_spec(1, width = "10em")

Item	Definitely Not	Probably Not	Possibly	Very Probably	Definitely
Sharing a research manuscript as a preprint	4.21	10.3	31	27	27
Publishing a paper or monograph book as open access	0.89	4.5	16	23	55
Preregistration of the study design, methods, hypothesis etc., prior to commencing the research	3.35	9.1	29	31	28
Open or FAIR data management and sharing (for research data, software, models, algorithms, workflows etc.)	0.90	2.7	14	26	56
Participation in open peer review (being reviewed or the reviewer)	1.81	6.3	30	27	35
Participation in public engagement, including citizen or community science	0.90	4.1	26	24	45
Collaboration via virtual research environments or virtual laboratories	0.50	7.9	46	27	19

Code

# plot
likert.bar.plot(items_likert, wrap = 30,
    centered = F, plot.percent.neutral = TRUE,
    plot.percent.low = TRUE, plot.percent.high = TRUE) +
    ggpubr::theme_cleveland() + theme(legend.position = "bottom")

Section 3

Does your institute/organisation have any initiative or tool which gives credits/rewards for Open Science activities?

Code

survey %>%
    group_by(does_your_institute_organisation_have_any_initiative_or_tool_which_gives_credits_rewards_for_open_science_activities) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(n/sum(n))) %>%
    rename(Responses = does_your_institute_organisation_have_any_initiative_or_tool_which_gives_credits_rewards_for_open_science_activities) %>%
    knitr::kable(digits = 2) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Responses	n	%
No	195	85%
Yes	35	15%

Code

# general
survey %>%
    rename(response = does_your_institute_organisation_have_any_initiative_or_tool_which_gives_credits_rewards_for_open_science_activities) %>%
    group_by(response) %>%
    count() %>%
    ggplot(aes(y = n, x = "", fill = response)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Set1") +
    theme_light() + labs(fill = "Does your institute/organisation\nhave any initiative or tool which\ngives credits/rewards for\nOpen Science activities?",
    x = "", y = "")

Code

# by country
survey %>%
    rename(response = does_your_institute_organisation_have_any_initiative_or_tool_which_gives_credits_rewards_for_open_science_activities,
        country = which_country_do_you_work_in) %>%
    group_by(response, country) %>%
    count() %>%
    filter(response == "Yes") %>%
    ggplot(aes(y = n, x = country, fill = country)) +
    geom_bar(stat = "identity") + scale_fill_brewer(palette = "Spectral") +
    scale_fill_manual(values = colorRampPalette(RColorBrewer::brewer.pal(9,
        "Set1"))(15)) + theme_light() + theme(axis.text.x = element_text(size = rel(0.5))) +
    labs(caption = "Mentioned initiative or tool which gives credits/rewards for Open Science activities by country",
        fill = "", x = "", y = "")

Section 4

How would you want the previously mentioned Open Science activities to be rewarded?

Labels

Code

rewardLabels <- survey %>%
    rename(labels = suggested_rewards_categorised_according_to_shar_cs_rewarding_terminology) %>%
    separate_rows(labels, sep = ";") %>%
    mutate(labels = str_squish(labels)) %>%
    filter(!is.na(labels) & labels != "") %>%
    mutate(labels = ifelse(labels == "good science'",
        "'good science'", labels)) %>%
    mutate(labels = ifelse(labels == "funding/grants for OS activitie",
        "funding/grants for OS activities",
        labels)) %>%
    mutate(labels = ifelse(labels == "OS indicators in research evaluation processes",
        "OS indicators in research evaluation and/or career progression processes",
        labels))

rewardLabels %>%
    group_by(labels) %>%
    count() %>%
    arrange(desc(n)) %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(`SHARC Rewards Terminology` = labels) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

SHARC Rewards Terminology	n	%
OS indicators in research evaluation and/or career progression processes	126	40.3%
funding/grants for OS activities	49	15.7%
awards/bonuses	33	10.5%
research visibility indicators	26	8.3%
authorship/contributorship	20	6.4%
capacity building for OS (e.g., training, raising awareness, provision of IT tools)	10	3.2%
research reputation	9	2.9%
acknowledgement/citation	8	2.6%
support through regulations and policy mandates	8	2.6%
collaboration (e.g., joint research, co-authorship)	7	2.2%
no specific reward	5	1.6%
contribution to ‘good’ science, research quality and integrity	3	1.0%
OS certifications/badges	2	0.6%
science as a public good	2	0.6%
CRediT taxonomy	1	0.3%
OS activities included in working hours	1	0.3%
championships/contests	1	0.3%
financial contribution to reviewers of OA journals	1	0.3%
punishment for 'closed' science	1	0.3%

Code

rewardLabels %>%
    filter(!is.na(labels)) %>%
    group_by(labels) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = fct_reorder(labels,
        n))) + geom_bar(stat = "identity",
    position = "fill") + guides(fill = guide_legend(ncol = 1)) +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_manual(values = colorRampPalette(RColorBrewer::brewer.pal(9,
        "Set1"))(19)) + theme_light() + labs(fill = "SHARC Rewards\nTerminology",
    x = "", y = "") + theme(legend.position = "right")

Code

most_common_rewards <- c("OS indicators in research evaluation and/or career progression processes",
    "funding/grants for OS activities", "awards/bonuses",
    "research visibility indicators", "authorship/contributorship")

rewardLabels %>%
    filter(!is.na(labels)) %>%
    mutate(labels = ifelse(!labels %in% most_common_rewards,
        "others", labels)) %>%
    group_by(labels) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = fct_relevel(fct_reorder(labels,
        n), "others"))) + geom_bar(stat = "identity",
    position = "fill") + guides(fill = guide_legend(ncol = 1)) +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Set1") +
    ggpubr::theme_cleveland() + labs(fill = "SHARC Rewards\nTerminology",
    x = "", y = "") + theme(legend.position = "right")

Respondents’ background

Gender

These are the different responses and how they were classified. I now added ‘non-binary’ as a different category and tagged all other as ‘other responses’.

Code

survey %>%
    distinct(your_gender, gender) %>%
    kable()

Gender responses

your_gender	gender
Femenino	female
M	male
-	no response
masculino	male
mujer	female
femenino	female
Masculino	male
Mujer	female
F	female
NA	no response
Female	female
female	female
FEMENINO	female
mujer cis	female
male	male
Male	male
Woman	female
남	male
여	female
féminin	female
woman	female
Why does that matter	no response
Genderqueer	non-binary / gender-queer
f	female
Female (she/her)	female
Female cis	female
Cis female	female
non-binary	non-binary / gender-queer
Non-binary	non-binary / gender-queer
feminine	female
Femail	female
not relevant	no response
male; trans man	male

• Your gender

Code

survey %>%
    group_by(gender) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(`Declared gender` = gender) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Declared gender	n	%
female	95	41.3%
male	82	35.7%
no response	50	21.7%
non-binary / gender-queer	3	1.3%

Code

survey %>%
    group_by(gender) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = gender)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Dark2") +
    theme_light() + labs(fill = "Gender",
    x = "", y = "")

Code

survey %>%
    rename(response = which_country_do_you_work_in) %>%
    mutate(response_x = ifelse(response !=
        "Korea, Rep." & response != "France" &
        response != "United States" & response !=
        "Uruguay", "Other countries", response)) %>%
    group_by(response_x) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(Country = response_x) %>%
    arrange(desc(n)) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Country	n	%
Other countries	86	37.4%
France	49	21.3%
United States	42	18.3%
Korea, Rep.	41	17.8%
Uruguay	12	5.2%

Country

• Which country do you work in?

Code

survey %>%
    group_by(which_country_do_you_work_in) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(Country = which_country_do_you_work_in) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Country	n	%
Albania	1	0.4%
Argentina	11	4.8%
Belgium	2	0.9%
Brazil	5	2.2%
Canada	1	0.4%
Chile	1	0.4%
China	2	0.9%
Colombia	2	0.9%
Croatia	1	0.4%
Czech Republic	1	0.4%
Denmark	2	0.9%
Estonia	1	0.4%
Finland	1	0.4%
France	49	21.3%
Germany	9	3.9%
Greece	1	0.4%
India	2	0.9%
Ireland	3	1.3%
Italy	2	0.9%
Korea, Rep.	41	17.8%
Lithuania	3	1.3%
Luxembourg	1	0.4%
Netherlands	7	3.0%
New Zealand	1	0.4%
Nigeria	3	1.3%
Poland	1	0.4%
Portugal	2	0.9%
Serbia	2	0.9%
Slovenia	1	0.4%
Spain	4	1.7%
Sweden	2	0.9%
Switzerland	1	0.4%
Turkey	1	0.4%
United Arab Emirates	1	0.4%
United Kingdom	8	3.5%
United States	42	18.3%
Uruguay	12	5.2%

Code

# which_country_do_you_work_in
survey %>%
    rename(response = which_country_do_you_work_in) %>%
    group_by(response) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = fct_reorder(response,
        desc(n)))) + geom_bar(stat = "identity") +
    scale_fill_manual(values = colorRampPalette(RColorBrewer::brewer.pal(9,
        "Set1"))(37)) + theme_light() + labs(caption = "Which country do you work in?",
    fill = "", x = "", y = "")

Code

# Korea, France, USA, Uruguay, and
# Other countries
survey %>%
    rename(response = which_country_do_you_work_in) %>%
    mutate(response_x = ifelse(response !=
        "Korea, Rep." & response != "France" &
        response != "United States" & response !=
        "Uruguay", "Other countries", response)) %>%
    group_by(response_x) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = fct_relevel(fct_reorder(response_x,
        n), "Other countries")), show.legend = FALSE) +
    geom_bar(stat = "identity", position = "fill") +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Spectral") +
    labs(fill = "") + theme_light() + labs(caption = "Which country do you work in?",
    x = "", y = "")

Code

survey %>%
    rename(response = which_country_do_you_work_in) %>%
    mutate(response_x = ifelse(response !=
        "Korea, Rep." & response != "France" &
        response != "United States" & response !=
        "Uruguay", "Other countries", response)) %>%
    group_by(response_x) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(Country = response_x) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Country	n	%
France	49	21.3%
Korea, Rep.	41	17.8%
Other countries	86	37.4%
United States	42	18.3%
Uruguay	12	5.2%

Map

Code

# map plot
world_survey %>%
    st_cast("POLYGON") %>%
    mutate(responses = ifelse(name == "France" &
        grepl("-52", geometry), NA, responses)) %>%
    mutate(name = ifelse(name == "France" &
        is.na(responses), "French Guyana",
        name)) %>%
    group_by(name) %>%
    dplyr::summarise(responses = max(responses)) %>%
    ggplot() + geom_sf(aes(fill = responses),
    size = 0.1) + scale_fill_fermenter(palette = "Spectral",
    breaks = c(0, 1, 2, 3, 4, 5, 10, 15,
        20, 25, 39), na.value = "grey95") +
    theme_minimal() + labs(fill = "N")

Job title

• Main position/job title: which of the following are you now?

Code

survey %>%
    rename(response = main_position_job_title_which_of_the_following_are_you_now) %>%
    group_by(response) %>%
    count() %>%
    arrange(desc(n)) %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(`Job title` = response) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Job title	n	%
Researcher	94	40.9%
Professor	35	15.2%
Other, please specify in the comment box	27	11.7%
Data specialist	22	9.6%
Graduate student (Master, PhD)	22	9.6%
Postdoc	15	6.5%
Policy officer	10	4.3%
Software engineer	5	2.2%

Experience in the field

• How long have you been working in your field?

Code

survey %>%
    group_by(how_long_have_you_been_working_in_your_field) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(`Experience in the field` = how_long_have_you_been_working_in_your_field) %>%
    mutate(index = ifelse(`Experience in the field` ==
        "> 20 years", 1, ifelse(`Experience in the field` ==
        "10-20 years", 2, ifelse(`Experience in the field` ==
        "5-10 years", 3, ifelse(`Experience in the field` ==
        "< 5 years", 4, 5))))) %>%
    arrange(index) %>%
    select(-index) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Experience in the field	n	%
> 20 years	61	26.5%
10-20 years	74	32.2%
5-10 years	56	24.3%
< 5 years	38	16.5%
NA	1	0.4%

Code

survey %>%
    rename(response = how_long_have_you_been_working_in_your_field) %>%
    group_by(response) %>%
    count() %>%
    filter(!is.na(response)) %>%
    ggplot(aes(x = "", y = n, fill = fct_relevel(response,
        "> 20 years", "10-20 years", "5-10 years",
        "< 5 years"))) + geom_bar(stat = "identity",
    position = "fill") + scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Spectral") +
    theme_light() + labs(fill = "Years of experience",
    x = "", y = "")

Affiliation type of organisation

• Please select the type of organisation you are primarily affiliated with

Code

survey %>%
    rename(response = please_select_the_type_of_organisation_you_are_primarily_affiliated_with) %>%
    group_by(response) %>%
    count() %>%
    arrange(desc(n)) %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(`Type of organisation` = response) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Type of organisation	n	%
University	90	39.1%
Research institute	86	37.4%
Government agency	29	12.6%
Other, please specify	12	5.2%
NGO / NPO	6	2.6%
International organisation	5	2.2%
Publisher (e.g., scholarly societies)	2	0.9%

Disciplinary fields

• What are your main disciplinary fields (up to 3; e.g., computer science, physics, biology, medicine)?

This plot is generated by classifying the disciplines according to Wikipedia’s Outline of academic disciplines.

Code

# disciplines
disciplines <- survey %>%
    rename(disciplines = starts_with("what_are_your_main_disciplinary_fields")) %>%
    select(response_id, disciplines) %>%
    separate_rows(disciplines, sep = ";") %>%
    mutate(disciplines = str_squish(disciplines) %>%
        str_to_lower())

new_wiki_disciplines <- read_csv("data/disciplines.csv")
disciplines_for_plot <- left_join(disciplines,
    new_wiki_disciplines)

disciplines_for_plot %>%
    filter(!is.na(subfield)) %>%
    group_by(field) %>%
    count() %>%
    arrange(desc(n)) %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(Field = field) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Field	n	%
Applied science	78	39.0%
Natural science	59	29.5%
Formal science	32	16.0%
Social science	23	11.5%
Humanities	8	4.0%

Code

disciplines_for_plot %>%
    filter(!is.na(subfield)) %>%
    group_by(field, subfield) %>%
    count() %>%
    arrange(field, desc(n)) %>%
    ungroup() %>%
    head(n = 25) %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(Field = field, `Sub-field` = subfield) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))

Field	Sub-field	n	%
Applied science	Medicine and health	21	17.8%
Applied science	Library and museum studies	10	8.5%
Applied science	Law	8	6.8%
Applied science	Public administration:Public policy	8	6.8%
Applied science	Environmental studies and forestry	5	4.2%
Applied science	Business	4	3.4%
Applied science	Engineering and technology: Chemical engineering	3	2.5%
Applied science	Engineering and technology: Materials science	3	2.5%
Applied science	Journalism, media studies and communication	3	2.5%
Applied science	Education	2	1.7%
Applied science	Engineering and technology: Mechanical engineering	2	1.7%
Applied science	Agriculture	1	0.8%
Applied science	Architecture and design	1	0.8%
Applied science	Engineering and technology	1	0.8%
Applied science	Engineering and technology: Civil engineering	1	0.8%
Applied science	Engineering and technology: Educational technology	1	0.8%
Applied science	Engineering and technology: Electrical engineering	1	0.8%
Applied science	Engineering and technology: Mechanical	1	0.8%
Applied science	Engineering and technology: Systems science	1	0.8%
Applied science	Public administration	1	0.8%
Formal science	Computer science	30	25.4%
Formal science	Mathematics	2	1.7%
Humanities	Philosophy	5	4.2%
Humanities	Languages and literature	2	1.7%
Humanities	History	1	0.8%

Code

disciplines_for_plot %>%
    filter(!is.na(subfield)) %>%
    group_by(field, subfield) %>%
    count() %>%
    PieDonut(aes(field, subfield, count = n),
        showPieName = F, showRatioThreshold = 0.02,
        labelposition = 1, ratioByGroup = F)

List of disciplines